Supervised Open Information Extraction

Gabriel Stanovsky, Julian Michael, Luke Zettlemoyer, Ido Dagan


Abstract
We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model outperforms the existing state-of-the-art Open IE systems on benchmark datasets.
Anthology ID:
N18-1081
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
885–895
Language:
URL:
https://aclanthology.org/N18-1081
DOI:
10.18653/v1/N18-1081
Bibkey:
Cite (ACL):
Gabriel Stanovsky, Julian Michael, Luke Zettlemoyer, and Ido Dagan. 2018. Supervised Open Information Extraction. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 885–895, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Supervised Open Information Extraction (Stanovsky et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1081.pdf
Data
AW-OIEQA-SRLQAMR